A Robust Machine Learning Model for Cyber Incident Classification and Prioritization

Abstract

Cyber incident classification and prioritization are crucial tasks in cybersecurity, enabling rapid response and resource allocation to mitigate potential threats effectively. This study presents a robust machine learning model designed for accurate classification and prioritization of cyber incidents, aiming to enhance cyber defense mechanisms. The proposed model integrates diverse machine learning algorithms, including Random Forest, Support Vector Machines, and Gradient Boosting, leveraging their complementary strengths to improve predictive performance and robustness. Extensive experimentation on real-world cyber threat datasets demonstrates the efficacy of the model, achieving high accuracy and reliability in identifying and prioritizing diverse types of cyber incidents. The model's performance is assessed using standard evaluation metrics such as accuracy, precision, recall, and F1-score, highlighting its ability to effectively distinguish between different classes of cyber threats and prioritize incidents based on their severity and potential impact on organizational assets. It was found that the model's interpretability is enhanced through feature importance analysis, providing insights into the key factors influencing cyber incident classification and prioritization decisions. The proposed machine learning model offers a promising approach to bolstering cyber defense capabilities, enabling organizations to proactively respond to cyber threats and safeguard their digital assets.